اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدLOLAS: an optical turbulence profiler in the atmospheric boundary layer with extreme altitude-resolution
221
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We report the development and first results of an instrument called Low Layer Scidar (LOLAS) which is aimed at the measurement of optical-turbulence profiles in the atmospheric boundary layer with high altitude-resolution. The method is based on the Generalized Scidar (GS) concept, but unlike the GS instruments which need a 1- m or larger telescope, LOLAS is implemented on a dedicated 40-cm telescope, making it an independent instrument. The system is designed for widely separated double-star targets, which enables the high altitude-resolution. Using a 20000-separation double- star, we have obtained turbulence profiles with unprecedented 12-m resolution. The system incorporates necessary novel algorithms for autoguiding, autofocus and image stabilisation. The results presented here were obtained at Mauna Kea Observatory. They show LOLAS capabilities but cannot be considered as representative of the site. A forthcoming paper will be devoted to the site characterisation. The instrument was built as part of the Ground Layer Turbulence Monitoring Campaign on Mauna Kea for Gemini Observatory.
قيم البحث
اقرأ أيضاً
In this work we compare the spectral properties of the daily medium temperature fluctuations with the experimental results of the Chicago Group, in which the local temperature fluctuations were measured in a helium cell. The results suggest that the
dynamics of the daily temperature fluctuations is determined by the oft turbulent state of the atmospheric boundary layer, which state is significantly different from low dimensional chaos.
We develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operat
or regression problem which characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier--Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using this data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.
The fluid dynamics video considers an array of two NREL 5-MW turbines separated by seven rotor diameters in a neutral atmospheric boundary layer (ABL). The neutral atmospheric boundary-layer flow data were obtained from a precursor ABL simulation usi
ng a Large-Eddy Simulation (LES) framework within OpenFOAM. The mean wind speed at hub height is 8m/s, and the surface roughness is 0.2m. The actuator line method (ALM) is used to model the wind turbine blades by means of body forces added to the momentum equation. The fluid dynamics video shows the root and tip vortices emanating from the blades from various viewpoints. The vortices become unstable and break down into large-scale turbulent structures. As the wakes of the wind turbines advect further downstream, smaller-scale turbulence is generated. It is apparent that vortices generated by the blades of the downstream wind turbine break down faster due to increased turbulence levels generated by the wake of the upstream wind turbine.
We describe a new model for image propagation through open air in the presence of changes in the index of refraction (e.g. due to turbulence) using the theory of optimal transport. We describe the relationship between photon density, or image intensi
ty, and the phase of the traveling wave and, together with a least action principle, suggest a method for approximately recovering the solution of the photon flow. By linking atmospheric propagation solutions to optimal transport, we provide a physics-based (as opposed to phenomenological) model for predicting turbulence-induced changes to sequences of images. Simulated and real data are utilized to validate and compare the model to other existing methods typically used to model this type of data. Given its superior performance in describing experimental data, the new model suggests new algorithms for a variety of atmospheric imaging applications.
The optical turbulence above Dome C in winter is mainly concentrated in the first tens of meters above the ground. Properties of this so-called surface layer (SL) were investigated during the period 2007-2012 by a set of sonics anemometers placed on
a 45 m high tower. We present the results of this long-term monitoring of the refractive index structure constant Cn2 within the SL, and confirm its thickness of 35m. We give statistics of the contribution of the SL to the seeing and coherence time. We also investigate properties of large scale structure functions of the temperature and show evidence of a second inertial zone at kilometric spatial scales.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
